Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

When and how java developers give up static type safety

The main goal of a static type system is to prevent certain kinds of errors from happening at run time. A type system is formulated as a set of constraints that gives any expression or term in a program a well-defined type. Besides detecting these kinds of errors, a static type system can be an invaluable maintenance tool, can be useful for documentation purposes, and can aid in generating more efficient machine code. However, there are situations when the developer has more information about the program that is too complex to explain in terms of typing constraints. To that end, programming languages often provide mechanisms that make the typing constraints less strict to permit more programs to be valid, at the expense of causing more errors at run time. These mechanisms are essentially two: Unsafe Intrinsics and Reflective Capabilities. We want to understand how and when developers give up these static constraints. This knowledge can be useful as: a) a recommendation for current and future language designers to make informed decisions, b) a reference for tool builders, e.g., by providing more precise or new refactoring analyses, c) a guide for researchers to test new language features, or to carry out controlled programming experiments, and d) a guide for developers for better practices. In this dissertation, we focus on the Unsafe API and cast operator---a subset of unsafe intrinsics and reflective capabilities respectively---in Java. We report two empirical studies to understand how these mechanisms---Unsafe API and cast operator---are used by Java developers when the static type system becomes too strict. We have devised usage patterns for both the Unsafe API and cast operator. Usage patterns are recurrent programming idioms to solve a specific issue. We believe that having usage patterns can help us to better categorize use cases and thus understand how those features are used

Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices

Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

An efficient implementation of the algorithm by Lukáš et al. on Hadoop

Apache Hadoop offers the possibility of coding full-fledged distributed applications with very low programming efforts. However, the resulting implementations may suffer from some performance bottlenecks that nullify the potential of a distributed system. An engineering methodology based on the implementation of smart optimizations driven by a careful profiling activity may lead to a much better experimental performance as shown in this paper. In particular, we take as a case study the algorithm by Lukáš et al. used to solve the Source Camera Identification problem (i.e., recognizing the camera used for acquiring a given digital image). A first implementation has been obtained, with little effort, using the default facilities available with Hadoop. A deep profiling allowed us to pinpoint some serious performance issues affecting the initial steps of the algorithm and related to a bad usage of the cluster resources. Optimizations were then developed and their effects were measured by accurate experimentation. The improved implementation is able to optimize the usage of the underlying cluster resources as well as of the Hadoop framework, thus resulting in a much better performance than the original naive implementation